Comparison with deformable convolution.
In [1]}, Dai et al. propose a deformable convolutional layer, which allows the sampling index set \(\mathcal {I}\)  to be non-grid and irregular. It assigns an offset for each index in \(\mathcal {I}\) , instead of only modifying the dilation rates. Compared with adaptive dilated convolution, deformable convolution enjoys higher degrees of freedom, but it also has a much higher computational cost. More importantly, the offsets introduced in deformable convolution are completely unconstrained and independent. This may cause the input and output feature maps to lose their spatial correspondence, which also potentially hurts the accuracy of location. We also experimentally proved in Sec REF  that the proposed adaptive dilated convolution is more suitable to human pose estimation than deformable convolution.
